{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Robust Linear Models"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# yahoo finance is used to fetch data \n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-09-11T03:28:31.191Z",
          "iopub.execute_input": "2021-09-11T03:28:31.197Z",
          "shell.execute_reply": "2021-09-11T03:28:31.981Z",
          "iopub.status.idle": "2021-09-11T03:28:31.989Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AMD'\n",
        "start = '2014-01-01'\n",
        "end = '2018-08-27'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "            Open  High   Low  Close  Adj Close    Volume\nDate                                                    \n2014-01-02  3.85  3.98  3.84   3.95       3.95  20548400\n2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200\n2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300\n2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100\n2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-02</th>\n      <td>3.85</td>\n      <td>3.98</td>\n      <td>3.84</td>\n      <td>3.95</td>\n      <td>3.95</td>\n      <td>20548400</td>\n    </tr>\n    <tr>\n      <th>2014-01-03</th>\n      <td>3.98</td>\n      <td>4.00</td>\n      <td>3.88</td>\n      <td>4.00</td>\n      <td>4.00</td>\n      <td>22887200</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>4.01</td>\n      <td>4.18</td>\n      <td>3.99</td>\n      <td>4.13</td>\n      <td>4.13</td>\n      <td>42398300</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>4.19</td>\n      <td>4.25</td>\n      <td>4.11</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>42932100</td>\n    </tr>\n    <tr>\n      <th>2014-01-08</th>\n      <td>4.23</td>\n      <td>4.26</td>\n      <td>4.14</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>30678700</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-09-11T03:28:33.215Z",
          "iopub.execute_input": "2021-09-11T03:28:33.221Z",
          "iopub.status.idle": "2021-09-11T03:28:33.970Z",
          "shell.execute_reply": "2021-09-11T03:28:33.983Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create more data\n",
        "dataset['Increase_Decrease'] = np.where(dataset['Volume'].shift(-1) > dataset['Volume'],1,0)\n",
        "dataset['Buy_Sell_on_Open'] = np.where(dataset['Open'].shift(-1) > dataset['Open'],1,-1)\n",
        "dataset['Buy_Sell'] = np.where(dataset['Adj Close'].shift(-1) > dataset['Adj Close'],1,-1)\n",
        "dataset['Return'] = dataset['Adj Close'].pct_change()\n",
        "dataset = dataset.dropna()\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "            Open  High   Low  Close  Adj Close    Volume  Increase_Decrease  \\\nDate                                                                          \n2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200                  1   \n2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300                  1   \n2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100                  0   \n2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700                  0   \n2014-01-09  4.20  4.23  4.05   4.09       4.09  30667600                  0   \n\n            Buy_Sell_on_Open  Buy_Sell    Return  \nDate                                              \n2014-01-03                 1         1  0.012658  \n2014-01-06                 1         1  0.032500  \n2014-01-07                 1        -1  0.012106  \n2014-01-08                -1        -1  0.000000  \n2014-01-09                -1         1 -0.021531  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n      <th>Increase_Decrease</th>\n      <th>Buy_Sell_on_Open</th>\n      <th>Buy_Sell</th>\n      <th>Return</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-03</th>\n      <td>3.98</td>\n      <td>4.00</td>\n      <td>3.88</td>\n      <td>4.00</td>\n      <td>4.00</td>\n      <td>22887200</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.012658</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>4.01</td>\n      <td>4.18</td>\n      <td>3.99</td>\n      <td>4.13</td>\n      <td>4.13</td>\n      <td>42398300</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.032500</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>4.19</td>\n      <td>4.25</td>\n      <td>4.11</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>42932100</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0.012106</td>\n    </tr>\n    <tr>\n      <th>2014-01-08</th>\n      <td>4.23</td>\n      <td>4.26</td>\n      <td>4.14</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>30678700</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>2014-01-09</th>\n      <td>4.20</td>\n      <td>4.23</td>\n      <td>4.05</td>\n      <td>4.09</td>\n      <td>4.09</td>\n      <td>30667600</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>1</td>\n      <td>-0.021531</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-09-11T03:28:35.700Z",
          "iopub.execute_input": "2021-09-11T03:28:35.705Z",
          "shell.execute_reply": "2021-09-11T03:28:35.733Z",
          "iopub.status.idle": "2021-09-11T03:28:35.718Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "(1170, 10)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-09-11T03:28:37.935Z",
          "iopub.execute_input": "2021-09-11T03:28:37.939Z",
          "iopub.status.idle": "2021-09-11T03:28:37.949Z",
          "shell.execute_reply": "2021-09-11T03:28:37.958Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x1 = dataset['Adj Close']"
      ],
      "outputs": [],
      "execution_count": 21,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-11T03:37:46.344Z",
          "iopub.execute_input": "2021-09-11T03:37:46.350Z",
          "iopub.status.idle": "2021-09-11T03:37:46.359Z",
          "shell.execute_reply": "2021-09-11T03:37:46.412Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nsample = 1170\n",
        "X = np.column_stack((x1, (x1 - 5) ** 2))\n",
        "X = sm.add_constant(X)\n",
        "sig = 0.3  # smaller error variance makes OLS<->RLM contrast bigger\n",
        "beta = [5, 0.5, -0.0]\n",
        "y_true2 = np.dot(X, beta)\n",
        "y2 = y_true2 + sig * 1.0 * np.random.normal(size=nsample)\n",
        "y2[[39, 41, 43, 45, 48]] -= 5  # add some outliers (10% of nsample)"
      ],
      "outputs": [],
      "execution_count": 22,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-11T03:37:47.278Z",
          "iopub.execute_input": "2021-09-11T03:37:47.283Z",
          "iopub.status.idle": "2021-09-11T03:37:47.291Z",
          "shell.execute_reply": "2021-09-11T03:37:47.299Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.api as sm\n",
        "\n",
        "res = sm.OLS(y2, X).fit()\n",
        "print(res.params)\n",
        "print(res.bse)\n",
        "print(res.predict())"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 4.96811018e+00  5.03727074e-01 -2.77582017e-04]\n",
            "[0.02667434 0.00523353 0.00062629]\n",
            "[ 6.98274089  7.04829295  7.07350261 ... 15.42583033 16.1132056\n",
            " 16.94748893]\n"
          ]
        }
      ],
      "execution_count": 23,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-09-11T03:38:01.381Z",
          "iopub.execute_input": "2021-09-11T03:38:01.389Z",
          "iopub.status.idle": "2021-09-11T03:38:01.400Z",
          "shell.execute_reply": "2021-09-11T03:38:01.412Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "resrlm = sm.RLM(y2, X).fit()\n",
        "print(resrlm.params)\n",
        "print(resrlm.bse)\n"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 5.00467345e+00  5.01424470e-01 -3.24367345e-04]\n",
            "[0.01899271 0.00372639 0.00044593]\n"
          ]
        }
      ],
      "execution_count": 24,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-11T03:38:10.506Z",
          "iopub.execute_input": "2021-09-11T03:38:10.512Z",
          "iopub.status.idle": "2021-09-11T03:38:10.526Z",
          "shell.execute_reply": "2021-09-11T03:38:10.537Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(12, 8))\n",
        "ax = fig.add_subplot(111)\n",
        "ax.plot(x1, y2, \"o\", label=\"data\")\n",
        "ax.plot(x1, y_true2, \"b-\", label=\"True\")\n",
        "pred_ols = res.get_prediction()\n",
        "iv_l = pred_ols.summary_frame()[\"obs_ci_lower\"]\n",
        "iv_u = pred_ols.summary_frame()[\"obs_ci_upper\"]\n",
        "\n",
        "ax.plot(x1, res.fittedvalues, \"r-\", label=\"OLS\")\n",
        "ax.plot(x1, iv_u, \"r--\")\n",
        "ax.plot(x1, iv_l, \"r--\")\n",
        "ax.plot(x1, resrlm.fittedvalues, \"g.-\", label=\"RLM\")\n",
        "ax.legend(loc=\"best\")"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 25,
          "data": {
            "text/plain": "<matplotlib.legend.Legend at 0x1842a897e10>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 864x576 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 25,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-11T03:38:23.993Z",
          "iopub.execute_input": "2021-09-11T03:38:24.000Z",
          "shell.execute_reply": "2021-09-11T03:38:24.248Z",
          "iopub.status.idle": "2021-09-11T03:38:24.256Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X2 = X[:, [0, 1]]\n",
        "res2 = sm.OLS(y2, X2).fit()\n",
        "print(res2.params)\n",
        "print(res2.bse)  "
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[4.97447111 0.50172366]\n",
            "[0.02247671 0.00263695]\n"
          ]
        }
      ],
      "execution_count": 26,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-09-11T03:38:38.466Z",
          "iopub.execute_input": "2021-09-11T03:38:38.473Z",
          "shell.execute_reply": "2021-09-11T03:38:38.497Z",
          "iopub.status.idle": "2021-09-11T03:38:38.485Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "resrlm2 = sm.RLM(y2, X2).fit()\n",
        "print(resrlm2.params)\n",
        "print(resrlm2.bse)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[5.01189302 0.49912998]\n",
            "[0.01596139 0.00187258]\n"
          ]
        }
      ],
      "execution_count": 27,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-09-11T03:38:45.473Z",
          "iopub.execute_input": "2021-09-11T03:38:45.479Z",
          "shell.execute_reply": "2021-09-11T03:38:45.503Z",
          "iopub.status.idle": "2021-09-11T03:38:45.492Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pred_ols = res2.get_prediction()\n",
        "iv_l = pred_ols.summary_frame()[\"obs_ci_lower\"]\n",
        "iv_u = pred_ols.summary_frame()[\"obs_ci_upper\"]\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(8, 6))\n",
        "ax.plot(x1, y2, \"o\", label=\"data\")\n",
        "ax.plot(x1, y_true2, \"b-\", label=\"True\")\n",
        "ax.plot(x1, res2.fittedvalues, \"r-\", label=\"OLS\")\n",
        "ax.plot(x1, iv_u, \"r--\")\n",
        "ax.plot(x1, iv_l, \"r--\")\n",
        "ax.plot(x1, resrlm2.fittedvalues, \"g.-\", label=\"RLM\")\n",
        "legend = ax.legend(loc=\"best\")"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 576x432 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 28,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-11T03:38:56.399Z",
          "iopub.execute_input": "2021-09-11T03:38:56.406Z",
          "shell.execute_reply": "2021-09-11T03:38:56.484Z",
          "iopub.status.idle": "2021-09-11T03:38:56.470Z"
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.13",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    },
    "nteract": {
      "version": "0.28.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}